Financial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.

Yves Hilpsch是Python Quants（德国）股份有限公司的创始人和任事股东，也是Python Quants（纽约）有限责任公司的共同创办人。该集团提供基于Python的金融和衍生品分析软件（参见http://pythonquants.com，http://quant-platfrom.com和http://dx-analytics.com），以及和Python及金融相关的咨询、开发和培训服务。 Yves还是Derivatives Analytics with Python（Wiley Finance，2015）的作者。作为获得数理金融学博士学位的商业管理专业研究生，他在萨尔州大学讲授计算金融学中的数值化方法课程。

This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.

"Corporate Finance", by Ross, Westerfield, and Jaffe emphasizes the modern fundamentals of the theory of finance, while providing contemporary examples to make the theory come to life. The authors aim to present corporate finance as the working of a small number of integrated and powerful intuitions, rather than a collection of unrelated topics. They develop the central concept...(展开全部)

本书系统介绍了SAS Viya的基础知识，并且介绍了如何用SAS Viya进行数据分析的完整过程，包括数据的读取、处理、展现和分析。同时介绍了如何使用其他开发语言系统便捷地利用SAS Viya强大的数据分析能力。

Yves Hilpsch是Python Quants（德国）股份有限公司的创始人和任事股东，也是Python Quants（纽约）有限责任公司的共同创办人。该集团提供基于Python的金融和衍生品分析软件（参见http://pythonquants.com，http://quant-platfrom.com和http://dx-analytics.com），以及和Python及金融相关的咨询、开发和培训服务。 Yves还是Derivatives Analytics with Python（Wiley Finance，2015）的作者。作为获得数理金融学博士学位的商业管理专业研究生，他在萨尔州大学讲授计算金融学中的数值化方法课程。